Description

You will build and maintain scalable tools and services for machine learning training and inference.

Responsibilities

• Implement continuous deployment using GitHub Actions and Flux.
• Develop and deploy cloud solutions, including MLOps pipelines on AWS.
• Containerize data science models using Docker and deploy them via Kubernetes.
• Communicate technical requirements between data scientists, engineers, and architects.

Required Skills

• 8+ years of experience in ML Ops with strong knowledge of Kubernetes, Python, MongoDB, and AWS.
• Experience building MLOps pipelines on cloud solutions (AWS).
• Proficiency with Linux administration and experience with software development and test automation.
• Experience developing containers and Kubernetes in cloud computing environments.
• Familiarity with data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.).
• Ability to design and implement cloud solutions and build custom integrations between cloud systems using APIs.
• Strong understanding of software testing, benchmarking, and continuous integration.
• Experience developing and maintaining ML systems built with open-source tools.
• Knowledge of ML models and LLM.

Key Skills

Education

Any Gradute